Local Differential Privacy (LDP) offers strong privacy protection, especially in settings in which the server collecting the data is untrusted. However, designing LDP mechanisms that achieve an optimal trade-off between privacy, utility and robustness to adversarial inference and integrity attacks remains challenging. In this work, we introduce a general multi-objective optimization framework for refining LDP protocols, enabling the joint optimization of privacy and utility under various adversarial settings. While our framework is flexible to accommodate multiple privacy and security attacks as well as utility metrics, in this paper, we specifically optimize for Attacker Success Rate (ASR) under \emph{data reconstruction attack} as a concrete measure of privacy leakage and Mean Squared Error (MSE) as a measure of utility. Complementarily, we evaluate integrity-oriented threats through data poisoning attacks, providing an additional adversarial perspective. More precisely, we systematically revisit these trade-offs by analyzing eight state-of-the-art LDP frequency estimation protocols and proposing refined counterparts that leverage tailored optimization techniques. Experimental results demonstrate that our proposed adaptive mechanisms consistently outperform their non-adaptive counterparts, achieving substantial reductions in ASR while preserving utility, and pushing closer to the ASR-MSE Pareto frontier. By bridging the gap between theoretical guarantees and real-world vulnerabilities, our framework enables modular and context-aware deployment of LDP mechanisms with tunable privacy-utility-attackability trade-offs.

Cryptographic approaches, such as secure multiparty computation, can be used to securely compute a function of a distributed graph without centralizing the data of each participant. However, the output of the protocol can leak sensitive information about the structure of the original graph. In particular, we propose an approach by which an adversary observing the result of a private protocol for the computation of the number of common neighbors between all pairs of vertices, can reconstruct the adjacency matrix of the graph. In fact, this can only be done up to co-squareness, a notion we introduce, as two different graphs can have the same matrix of common neighbors. To realize this, we consider two adversary models, one who observes the common neighbors matrix only and a more informed one that has partial knowledge of the original graph. Our results demonstrate that, from their common neighbors matrix, graphs can be reconstructed with high accuracy (up to co-squareness). The proposed reconstruction is also interesting in itself from the point of view of graph theory.

This special day event on Responsible Artificial Intelligence (AI) brings together researchers, practitioners, and policymakers to explore how data mining and machine learning systems can be designed to align with ethical principles, societal values, and human well-being. As AI technologies increasingly influence decisions in healthcare, finance, governance, and social systems, there is a critical need to develop frameworks that embed fairness, accountability, and privacy directly into the foundations of knowledge discovery. This full-day event will feature a mix of invited talks, interactive debates, expert panels, and peer-reviewed research presentations, all focused on the practical integration of ethical design into data-driven systems. The Responsible AI Day builds on the success of Canada's NSERC CREATE Program on Responsible AI, an interdisciplinary initiative training the next generation of AI researchers across computer science, law, bioethics, public health, and media studies. Topics will span scalable AI governance, privacy-preserving computation, algorithmic bias mitigation, and the socio-legal tensions emerging in generative AI. By positioning responsible AI as a sociotechnical challenge, this special day aligns with KDD's mission of advancing data science that is not only technically robust but also socially conscious.

While there were already some privacy challenges organized in the domain of data sanitization, they have mainly focused on the defense side of the problem. To favor the organization of successful challenges focusing on attacks, we introduce the SNAKE framework that is designed to facilitate the organization of challenges dedicated to attacking existing data sanitization mechanisms. In particular, it enables to easily automate the redundant tasks that are inherent to any such challenge and exhibits the following salient features: genericity with respect to attacks, ease of use and extensibility. We propose to demonstrate the main features of the SNAKE framework through a specific instantiation focusing on membership inference attacks over differentially-private synthetic data generation schemes. This instance of the SNAKE framework is currently being used for supporting a challenge co-located with APVP 2023 (the French workshop on the protection of privacy).

International audience

The private collection of multiple statistics from a population is a fundamental statistical problem. One possible approach to realize this is to rely on the local model of differential privacy (LDP). Numerous LDP protocols have been developed for the task of frequency estimation of single and multiple attributes. These studies mainly focused on improving the utility of the algorithms to ensure the server performs the estimations accurately. In this paper, we investigate privacy threats (re-identification and attribute inference attacks) against LDP protocols for multidimensional data following two state-of-the-art solutions for frequency estimation of multiple attributes. To broaden the scope of our study, we have also experimentally assessed five widely used LDP protocols, namely, generalized randomized response, optimal local hashing, subset selection, RAPPOR and optimal unary encoding. Finally, we also proposed a countermeasure that improves both utility and robustness against the identified threats. Our contributions can help practitioners aiming to collect users' statistics privately to decide which LDP mechanism best fits their needs.

FairCORELS is an open-source Python module for building fair rule lists. It is a multi-objective variant of CORELS, a branch-and-bound algorithm to learn certifiably optimal rule lists. FairCORELS supports six statistical fairness metrics, proposes several exploration parameters and leverages on the fairness constraints to prune the search space efficiently. It can easily generate sets of accuracy-fairness trade-offs. The models learnt are interpretable by design and a sparsity parameter can be used to control their length.

Location is one of the most extensively collected personal data on mobile by applications and third-party services. However, how the location of users is actually processed in practice by the actors of targeted advertising ecosystem remains unclear. Nonetheless, these providers have a strong incentive to create very detailed profile of users to better monetize the collected data. End users are usually not aware about the strength and wide range of inference that can be performed from their mobility traces. In this demonstration, users interact with a web-based application to inspect their location history and to discover the inferential power of this kind of data. Moreover to better understand the possible countermeasures, users can apply a sanitization to protect their data and visualize the impact on both the mobility traces and the associated inferred information. The objective of this demonstration is to raise the user awareness on the profiling capabilities and the privacy threats associated with disclosing his location data as well as how sanitization mechanisms can be efficient to mitigate these privacy risks. In addition, by collecting users feedbacks on the personal information revealed and the usage of a geosanitization mechanism, we hope that this demonstration will also be useful to constitute a new and valuable dataset on users perceptions on these questions.

Since their introduction, SNS (Social Networking Sites) such as MySpace, Facebook and LinkedIn have attracted millions of users and have become established places for keeping contact with old acquaintances and meeting new ones. Nonetheless, due to lack of user awareness and proper privacy protection tools, huge quantities of user data, including personal information, pictures and videos are quickly falling into the hands of authorities, strangers, recruiters and even the public at large. By using SNSs and accepting their privacy policy, users have volunteered to relinquish their ownership on their own data, which explains why the proposed privacy solutions based on current SNSs cannot solve all user privacy issues. As such, we start by setting the foundations for privacy and introduce a Privacy Framework for SNSs. Then, based on this framework, we present a User Privacy Policy (UPP) which provides users with an easy and flexible way to specify and communicate their privacy concerns to other users, third parties and to the SNS provider.